Machine Learning Deployment Is The Biggest Tech Trend In 2021

"What good is an ML model if it isn't fast? Having machine learning in a company's portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster. According to PwC, AI's potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the company's total funding to $47 million. For OctoML's CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. "It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware.